Quantitative Driving Style Estimation for Energy-Oriented Applications in Road Vehicles

Abstract

Energy-consumption in ground transportation systems is a major cause of CO2 emissions and related environmental concerns. A relevant part of such an energy use can be saved by devising systems that can help promoting an economical driving style. To address this challenging issue, this paper presents a method and system that allow providing a quantitative description the driving style and illustrating it to the driver by means of realtime visual feedback. The driving style assessment is based on the definition of appropriate energy-oriented cost functions that can be evaluated based on inertial measurements only, thereby providing a vehicle-independent methodology. The effectiveness of the approach, and the savings enabled by the interaction with the driver are assessed with an experimental campaign carried out on urban and extra-urban routes by different drivers.

Driving Style Recognition for Intelligent Vehicle Control and Advanced Driver Assistance: A Survey

“…Fuel efficiency is mainly influenced, although not exclusively by: vehicle characteristics, road type, traffic conditions and driving pattern [13] [19 -21]. Albeit it is wellknown that DS strongly affects fuel efficiency, there is still limited knowledge of the direct relationship [11].…”

“…Furthermore, the society is becoming more aware of environmental concerns, which influences decisions made by industry and end users [11] [12]. Particularly, in 2004 27% of the global CO2 emissions were caused by the transport sector, being 83% responsibility of road vehicles [13]. Eco-friendliness, fuel supply security and CO2 regulations are driving research towards more efficient powertrains including: conventional, battery electrical vehicles (BEVs), hybrid electric vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs) between others [12].…”

“…The firsts can intervene into the driving task directly by generating haptic inputs [2] [3] [7] [9], whilst the second is only advisory and targets to improve drivers' awareness through visual or audio advice [15] [16]. These systems that provide only feedback to the driver, either haptic or passive, are classified as passive, whilst active systems can interfere directly in the vehicle operation by modifying driver's power demand and recalibrating the components response to compensate for deficient driving habits [13] [16]. Although the feedback suitability and effectiveness can be argued, it has been observed that the simple fact of being monitored already encourages drivers to correct their style [17] [18].…”

Green Move: A Platform for Highly Configurable, Heterogeneous Electric Vehicle Sharing

“…Thus, we defined 1 c as the high-pass filter of , Pwheel evaluating the smoothness of the power profile chosen by the driver. In fact, the power spent at the wheel Pwheel h can be split into two parts: a low-frequency one, , PLPF which is needed to move the vehicle, and a high-frequency one, PHPF that represents unnecessary inertial power that is wasted in the process [16]. Fig.…”

“…The detailed formulation of the indicators and a discussion of the implementation of the algorithm can be found in [16].…”